Prediction of student performance in abacus-based calculation using matrix factorization
K Tokuda, D Kaschub, T Ota, Y Hashimoto… - Adjunct Publication of …, 2020 - dl.acm.org
K Tokuda, D Kaschub, T Ota, Y Hashimoto, N Fujiwara, A Sudo
Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation …, 2020•dl.acm.orgWe conducted modeling of student learning status and tasks in abacus-based calculation by
utilizing matrix factorization on student-generated learning data. The matrix consisted of
performance scores on student-task pairs. We decomposed the raw matrix into two matrices,
yielding the distributed representations of each student and each task. Prediction of student
performance using those decomposed matrices achieved better results than baseline
models that use the student biases and task biases. This suggests matrix factorization …
utilizing matrix factorization on student-generated learning data. The matrix consisted of
performance scores on student-task pairs. We decomposed the raw matrix into two matrices,
yielding the distributed representations of each student and each task. Prediction of student
performance using those decomposed matrices achieved better results than baseline
models that use the student biases and task biases. This suggests matrix factorization …
We conducted modeling of student learning status and tasks in abacus-based calculation by utilizing matrix factorization on student-generated learning data. The matrix consisted of performance scores on student-task pairs. We decomposed the raw matrix into two matrices, yielding the distributed representations of each student and each task. Prediction of student performance using those decomposed matrices achieved better results than baseline models that use the student biases and task biases. This suggests matrix factorization successfully extracted the interaction of multiple latent features of each task and each student's learning status in abacus-based calculation.
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